Landmines remain a threat to war-affected communities for years after conflicts have ended, partly due to the laborious nature of demining tasks. Humanitarian demining operations begin by collecting relevant information from the sites to be cleared, which is then analyzed by human experts to determine the potential risk of remaining landmines. In this paper, we propose RELand system to support these tasks, which consists of three major components. We (1) provide general feature engineering and label assigning guidelines to enhance datasets for landmine risk modeling, which are widely applicable to global demining routines, (2) formulate landmine presence as a classification problem and design a novel interpretable model based on sparse feature masking and invariant risk minimization, and run extensive evaluation under proper protocols that resemble real-world demining operations to show a significant improvement over the state-of-the-art, and (3) build an interactive web interface to suggest priority areas for demining organizations. We are currently collaborating with a humanitarian demining NGO in Colombia that is using our system as part of their field operations in two areas recently prioritized for demining.

翻译：地雷在冲突结束后多年仍威胁着受战争影响社区的安全，部分原因是排雷任务本身极其繁重。人道主义排雷行动首先从待清理区域收集相关信息，再由人类专家分析这些信息以确定剩余地雷的潜在风险。本文提出RELand系统以支持此类任务，该系统包含三大核心组件：我们（1）提供通用特征工程与标签分配指南，以增强用于地雷风险建模的数据集，这些指南广泛适用于全球排雷流程；（2）将地雷存在性建模为分类问题，设计基于稀疏特征掩蔽与不变风险最小化的新型可解释模型，并在模拟真实排雷操作的严格协议下进行广泛评估，结果表明该方法显著优于现有技术；（3）构建交互式网络界面，为排雷组织建议优先处理区域。目前我们正与哥伦比亚一家人道主义排雷非政府组织合作，该系统已在其两个近期优先排雷区域的实地行动中投入使用。